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Predicting Outcomes of Case-based Legal Arguments Stefanie Bruninghaus and Kevin D. Ashley
 

Summary: Predicting Outcomes of Case-based Legal Arguments
Stefanie Bršuninghaus and Kevin D. Ashley
Learning Research and Development Center,
Intelligent Systems Program, School of Law
University of Pittsburgh, Pittsburgh, PA 15260
steffi@pitt.edu, ashley@pitt.edu
ABSTRACT
In this paper, we introduce IBP, an algorithm that combines
reasoning with an abstract domain model and case-based
reasoning techniques to predict the outcome of case-based
legal arguments. Unlike the predictions generated by statis-
tical or machine-learning techniques, IBP's predictions are
accompanied by explanations.
We describe an empirical evaluation of IBP, in which we
compare our algorithm to prediction based on Hypo's and
CATO's relevance criteria, and to a number of widely used
machine learning algorithms. IBP reaches higher accuracy
than all competitors, and hypothesis testing shows that the
observed differences are statistically significant. An abla-
tion study indicates that both sources of knowledge in IBP

  

Source: Ashley, Kevin D. - Learning Research and Development Center, University of Pittsburgh

 

Collections: Computer Technologies and Information Sciences